The number of international tourist arrivals in Australia was forecast to continuously increase between 2024 and 2029 by in total *** million arrivals (+***** percent). After the ninth consecutive increasing year, the arrivals is estimated to reach ***** million arrivals and therefore a new peak in 2029. Depicted is the number of inbound international tourists. According to World Bank this refers to tourists travelling to a country which is not their usual residence, whereby the main purpose is not work related and the planned visitation period does not exceed 12 months. The forecast has been adjusted for the expected impact of COVID-19.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the number of international tourist arrivals in countries like New Zealand and Fiji.

This dataset presents statistics about the top international markets for tourism to specific Local Government Areas (LGA) around Australia. The LGAs covered in the data are a subset of the LGA boundaries classified in the 2016 Australian Statistical Geography Standard (ASGS). The data presents the top three origin countries which produce the highest number of international tourism visitors to the specified LGA. Data relating to the number of visitors and nights stayed by these visitors are provided for each of the three origin countries. The data values are representative of a yearly average based on the four years of: 2013, 2014, 2015 and 2016. Tourism Research Australia (TRA) first developed Local Government Area tourism profiles in 2007 to assist industry and Government decision making and to identify and support investment opportunities, particularly in regional Australia. The latest profiles provide an update for over 200 Local Government Areas. Data are drawn from TRA's International Visitor Survey (IVS) and National Visitor Survey (NVS), along with demographic and business data from the Australian Bureau of Statistics (ABS). Profiles were only prepared for Local Government Areas with adequate International Visitor Survey (IVS) and National Visitor Survey (NVS) sample to present robust results. Further, data are averaged over four years, which minimises the impact of variability in estimates from year to year, and provides for more robust volume estimates. For more information please visit the Website of the TRA. Please note: AURIN has spatially enabled the original data.

Australia Visitor Arrivals: By Country: South East Asia: Singapore data was reported at 21,840.000 Movement in Feb 2025. This records an increase from the previous number of 16,510.000 Movement for Jan 2025. Australia Visitor Arrivals: By Country: South East Asia: Singapore data is updated monthly, averaging 20,920.000 Movement from Jan 1991 (Median) to Feb 2025, with 410 observations. The data reached an all-time high of 59,040.000 Movement in Jun 2019 and a record low of 60.000 Movement in Apr 2020. Australia Visitor Arrivals: By Country: South East Asia: Singapore data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.Q006: Visitor Arrivals: Short Term: by Countries. [COVID-19-IMPACT]

Please Note: As announced by the Minister for Immigration and Border Protection on 25 June 2017, the Department of Immigration and Border Protection (DIBP) retired the paper-based Outgoing Passenger Cards (OPC) from 1 July 2017. The information previously gathered via paper-based outgoing passenger cards is now be collated from existing government data and will continue to be provided to users. Further information can be accessed here: http://www.minister.border.gov.au/peterdutton/Pages/removal-of-the-outgoing-passenger-card-jun17.aspx.

Due to the retirement of the OPC, the Australian Bureau of Statistics (ABS) undertook a review of the OAD data based on a new methodology. Further information on this revised methodology is available at: http://www.abs.gov.au/AUSSTATS/abs@.nsf/Previousproducts/3401.0Appendix2Jul%202017?opendocument&tabname=Notes&prodno=3401.0&issue=Jul%202017&num=&view=

A sampling methodology has been applied to this dataset. This method means that data will not replicate, exactly, data released by the ABS, but the differences should be negligible.

Due to ‘Return to Source’ limitations, data supplied to ABS from non-DIPB sources are also excluded.

Overseas Arrivals and Departures (OAD) data refers to the arrival and departure of Australian residents or overseas visitors, through Australian airports and sea ports, which have been recorded on incoming or outgoing passenger cards. OAD data describes the number of movements of travellers rather than the number of travellers. That is, multiple movements of individual persons during a given reference period are all counted. OAD data will differ from data derived from other sources, such as Migration Program Outcomes, Settlement Database or Visa Grant information. Travellers granted a visa in one year may not arrive until the following year, or may not travel to Australia at all. Some visas permit multiple entries to Australia, so travellers may enter Australia more than once on a visa. Settler Arrivals includes New Zealand citizens and other non-program settlers not included on the Settlement Database. The Settlement Database includes onshore processed grants not included in Settler Arrivals.

These de-identified statistics are periodically checked for privacy and other compliance requirements. The statistics were temporarily removed in March 2024 in response to a question about privacy within the emerging technological environment. Following a thorough review and risk assessment, the Department of Home Affairs has republished the dataset.

This dataset presents statistics regarding tourism to specific Local Government Areas (LGA) around Australia. The LGAs covered in the data are a subset of the LGA boundaries classified in the 2018 Australian Statistical Geography Standard (ASGS). The data presents statistics for reason for visit, travel party type and accommodation details for trips to the specified LGAs by their location of origin and visit duration. The data values are representative of a yearly average based on the four years of: 2015, 2016, 2017 and 2018. Tourism Research Australia (TRA) first developed Local Government Area tourism profiles in 2007 to assist industry and Government decision making and to identify and support investment opportunities, particularly in regional Australia. The latest profiles provide an update for over 200 Local Government Areas. Data are drawn from TRA's International Visitor Survey (IVS) and National Visitor Survey (NVS), along with demographic and business data from the Australian Bureau of Statistics (ABS). Profiles were only prepared for Local Government Areas with adequate IVS and NVS sample to present robust results. Further, data are averaged over four years, which minimises the impact of variability in estimates from year to year, and provides for more robust volume estimates. For more information please visit TRA. Please note: AURIN has spatially enabled the original data.

This dataset presents statistics about the top international markets for tourism to specific Local Government Areas (LGA) around Australia. The LGAs covered in the data are a subset of the LGA boundaries classified in the 2018 Australian Statistical Geography Standard (ASGS). The data presents the top three origin countries which produce the highest number of international tourism visitors to the specified LGA. Data relating to the number of visitors and nights stayed by these visitors are provided for each of the three origin countries. The data values are representative of a yearly average based on the four years of: 2015, 2016, 2017 and 2018. Tourism Research Australia (TRA) first developed Local Government Area tourism profiles in 2007 to assist industry and Government decision making and to identify and support investment opportunities, particularly in regional Australia. The latest profiles provide an update for over 200 Local Government Areas. Data are drawn from TRA's International Visitor Survey (IVS) and National Visitor Survey (NVS), along with demographic and business data from the Australian Bureau of Statistics (ABS). Profiles were only prepared for Local Government Areas with adequate IVS and NVS sample to present robust results. Further, data are averaged over four years, which minimises the impact of variability in estimates from year to year, and provides for more robust volume estimates. For more information please visit TRA. Please note: AURIN has spatially enabled the original data.

This table contains 45 series, with data for years 2014 - 2014 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) Countries visited (15 items: United States; Mexico; United Kingdom; France; ...) Travel characteristics (3 items: Visits; Nights; Spending in country).

SA3 based data for Country of Birth of Person by Year of Arrival in Australia, in Place of Enumeration Profile (PEP), 2016 Census. Count of persons born overseas (excludes overseas visitors). P10 is broken up into 3 sections (P10a - P10c), this section contains 'South Africa Year of arrival Before 1946' - 'Total Total'. Where arrival is stated as the year 2016 it corresponds to the period 1 January 2016 to 9 August 2016. The list of countries consists of the most common Country of Birth responses (excluding Australia) reported in the 2011 Census. The data is by SA3 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census.

This Australian and New Zealand food category cost dataset was created to inform diet and economic modelling for low and medium socioeconomic households in Australia and New Zealand. The dataset was created according to the INFORMAS protocol, which details the methods to systematically and consistently collect and analyse information on the price of foods, meals and affordability of diets in different countries globally. Food categories were informed by the Food Standards Australian New Zealand (FSANZ) AUSNUT (AUStralian Food and NUTrient Database) 2011-13 database, with additional food categories created to account for frequently consumed and culturally important foods.

Methods The dataset was created according to the INFORMAS protocol [1], which detailed the methods to collect and analyse information systematically and consistently on the price of foods, meals, and affordability of diets in different countries globally.

Cost data were collected from four supermarkets in each country: Australia and New Zealand. In Australia, two (Coles Merrylands and Woolworths Auburn) were located in a low and two (Coles Zetland and Woolworths Burwood) were located in a medium metropolitan socioeconomic area in New South Wales from 7-11th December 2020. In New Zealand, two (Countdown Hamilton Central and Pak ‘n Save Hamilton Lake) were located in a low and two (Countdown Rototuna North and Pak ‘n Save Rosa Birch Park) in a medium socioeconomic area in the North Island, from 16-18th December 2020.

Locations in Australia were selected based on the Australian Bureau of Statistics Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD) [2]. The index ranks areas from most disadvantaged to most advantaged using a scale of 1 to 10. IRSAD quintile 1 was chosen to represent low socio-economic status and quintile 3 for medium SES socio-economic status. Locations in New Zealand were chosen using the 2018 NZ Index of Deprivation and statistical area 2 boundaries [3]. Low socio-economic areas were defined by deciles 8-10 and medium socio-economic areas by deciles 4-6. The supermarket locations were chosen according to accessibility to researchers. Data were collected by five trained researchers with qualifications in nutrition and dietetics and/or nutrition science.

All foods were aggregated into a reduced number of food categories informed by the Food Standards Australian New Zealand (FSANZ) AUSNUT (AUStralian Food and NUTrient Database) 2011-13 database, with additional food categories created to account for frequently consumed and culturally important foods. Nutrient data for each food category can therefore be linked to the Australian Food and Nutrient (AUSNUT) 2011-13 database [4] and NZ Food Composition Database (NZFCDB) [5] using the 8-digit codes provided for Australia and New Zealand, respectively.

Data were collected for three representative foods within each food category, based on criteria used in the INFORMAS protocol: (i) the lowest non-discounted price was chosen from the most commonly available product size, (ii) the produce was available nationally, (iii) fresh produce of poor quality was omitted. One sample was collected per representative food product per store, leading to a total of 12 food price samples for each food category. The exception was for the ‘breakfast cereal, unfortified, sugars ≤15g/100g’ food category in the NZ dataset, which included only four food price samples because only one representative product per supermarket was identified.

Variables in this dataset include: (i) food category and description, (ii) brand and name of representative food, (iii) product size, (iv) cost per product, and (v) 8-digit code to link product to nutrient composition data (AUSNUT and NZFCDB).

References

Vandevijvere, S.; Mackay, S.; Waterlander, W. INFORMAS Protocol: Food Prices Module [Internet]. Available online: https://auckland.figshare.com/articles/journal_contribution/INFORMAS_Protocol_Food_Prices_Module/5627440/1 (accessed on 25 October).
2071.0 - Census of Population and Housing: Reflecting Australia - Stories from the Census, 2016 Available online: https://www.abs.gov.au/ausstats/abs@.nsf/Lookup/by Subject/2071.0~2016~Main Features~Socio-Economic Advantage and Disadvantage~123 (accessed on 10 December).
Socioeconomic Deprivation Indexes: NZDep and NZiDep, Department of Public Health. Available online: https://www.otago.ac.nz/wellington/departments/publichealth/research/hirp/otago020194.html#2018 (accessed on 10 December)
AUSNUT 2011-2013 food nutrient database. Available online: https://www.foodstandards.gov.au/science/monitoringnutrients/ausnut/ausnutdatafiles/Pages/foodnutrient.aspx (accessed on 15 November).
NZ Food Composition Data. Available online: https://www.foodcomposition.co.nz/ (accessed on 10 December)

Usage Notes The uploaded data includes an Excel spreadsheet where a separate worksheet is provided for the Australian food price database and New Zealand food price database, respectively. All cost data are presented to two decimal points, and the mean and standard deviation of each food category is presented. For some representative foods in NZ, the only NFCDB food code available was for a cooked product, whereas the product is purchased raw and cooked prior to eating, undergoing a change in weight between the raw and cooked versions. In these cases, a conversion factor was used to account for the weight difference between the raw and cooked versions, to ensure that nutrient information (on accessing from the NZFCDB) was accurate. This conversion factor was developed based on the weight differences between the cooked and raw versions, and checked for accuracy by comparing quantities of key nutrients in the cooked vs raw versions of the product.

SA4 based data for Country of Birth of Person by Year of Arrival in Australia, in Place of Enumeration Profile (PEP), 2016 Census. Count of persons born overseas (excludes overseas visitors). P10 is broken up into 3 sections (P10a - P10c), this section contains 'South Africa Year of arrival Before 1946' - 'Total Total'. Where arrival is stated as the year 2016 it corresponds to the period 1 January 2016 to 9 August 2016. The list of countries consists of the most common Country of Birth responses (excluding Australia) reported in the 2011 Census. The data is by SA4 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census.

Please note this dataset is the most recent version of the Administrative Boundaries (AB). For previous versions of the AB please go to this url: https://data.gov.au/dataset/ds-dga-b4ad5702-ea2b-4f04-833c-d0229bfd689e/details?q=previous

Geoscape Administrative Boundaries is Australia’s most comprehensive national collection of boundaries, including government, statistical and electoral boundaries. It is built and maintained by Geoscape Australia using authoritative government data. Further information about contributors to Administrative Boundaries is available here.

This dataset comprises seven Geoscape products:

• Localities
• Local Government Areas (LGAs)
• Wards
• Australian Bureau of Statistics (ABS) Boundaries
• Electoral Boundaries
• State Boundaries and
• Town Points

Updated versions of Administrative Boundaries are published on a quarterly basis.

Users have the option to download datasets with feature coordinates referencing either GDA94 or GDA2020 datums.

Notable changes in the May 2025 release

• Victorian Wards have seen almost half of the dataset change now reflecting the boundaries from the 2024 subdivision review. https://www.vec.vic.gov.au/electoral-boundaries/council-reviews/ subdivision-reviews.

  • There have been spatial changes (area) greater than 1 km2 to 66 wards in Victoria.

• One new locality ‘Kenwick Island’ has been added to the local Government area ‘Mackay Regional’ in Queensland.

  • There have been spatial changes(area) greater than 1 km2 to the local government areas 'Burke Shire' and 'Mount Isa City' in Queensland.

• There have been spatial changes(area) greater than 1 km2 to the localities ‘Nicholson’, ‘Lawn Hill’ and ‘Coral Sea’ in Queensland and ‘Calguna’, ‘Israelite Bay’ and ‘Balladonia’ in Western Australia.

• An update to the NT Commonwealth Electoral Boundaries has been applied to reflect the redistribution of the boundaries gazetted on 4 March 2025.

• Geoscape has become aware that the DATE_CREATED and DATE_RETIRED attributes in the commonwealth_electoral_polygon MapInfo TAB tables were incorrectly ordered and did not match the product data model. These attributes have been re-ordered to match the data model for the May 2025 release.

IMPORTANT NOTE: correction of issues with the 22 November 2022 release

• On 28 November 2022, the Administrative Boundaries dataset originally released on 22 November 2022 was amended and re-uploaded after Geoscape identified some issues with the original data for 'Electoral Boundaries'.
• As a result of the error, some shapefiles were published in 3D rather than 2D, which may affect some users when importing data into GIS applications.
• The error affected the Electoral Boundaries dataset, specifically the Commonwealth boundary data for Victoria and Western Australia, including 'All States'.
• Only the ESRI Shapefile formats were affected (both GDA94 and GDA2020). The MapInfo TAB format was not affected.
• Because the datasets are zipped into a single file, once the error was fixed by Geoscape all of Administrative Boundaries shapefiles had to be re-uploaded, rather than only the affected files.
• If you downloaded either of the two Administrative Boundary ESRI Shapefiles between 22 November and 28 November 2022 and plan to use the Electoral Boundary component, you are advised to download the revised version dated 28 November 2022. Apologies for any inconvenience.

Further information on Administrative Boundaries, including FAQs on the data, is available here or through Geoscape Australia’s network of partners. They provide a range of commercial products based on Administrative Boundaries, including software solutions, consultancy and support.

Note: On 1 October 2020, PSMA Australia Limited began trading as Geoscape Australia.

The Australian Government has negotiated the release of Administrative Boundaries to the whole economy under an open CCBY 4.0 licence.

Users must only use the data in ways that are consistent with the Australian Privacy Principles issued under the Privacy Act 1988 (Cth).

Users must also note the following attribution requirements:

Preferred attribution for the Licensed Material:

Administrative Boundaries © Geoscape Australia licensed by the Commonwealth of Australia under Creative Commons Attribution 4.0 International license (CC BY 4.0).

Preferred attribution for Adapted Material:

Incorporates or developed using Administrative Boundaries © Geoscape Australia licensed by the Commonwealth of Australia under Creative Commons Attribution 4.0 International licence (CC BY 4.0).

What to Expect When You Download Administrative Boundaries

Administrative Boundaries is large dataset (around 1.5GB unpacked), made up of seven themes each containing multiple layers.

Users are advised to read the technical documentation including the product change notices and the individual product descriptions before downloading and using the product.

Please note this dataset is the most recent version of the Administrative Boundaries (AB). For previous versions of the AB please go to this url: https://data.gov.au/dataset/ds-dga-b4ad5702-ea2b-4f04-833c-d0229bfd689e/details?q=previous

License Information

This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools. The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011). The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels. References: Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5

This feature layer provides access to OpenStreetMap (OSM) tourist attraction point data for Australia and Oceania, which is updated every 5 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM point (node) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes tourism features defined as a query against the hosted feature layer (i.e. tourism is not blank).In OSM, tourism features are places and things of specific interest to tourists including places to see, places to stay, things and places providing information and support to tourists. These features are identified with a tourism tag. There are hundreds of different tag values used in the OSM database. In this feature layer, unique symbols are used for several of the most popular tourism types, while lesser used types are grouped in an "other" category.Zoom in to large scales (e.g. Cities level or 1:160k scale) to see the tourism features display. You can click on a feature to get the name of the tourism feature. The name of the feature will display by default at very large scales (e.g. Building level of 1:2k scale). Labels can be turned off in your map if you prefer.Create New LayerIf you would like to create a more focused version of this tourism layer displaying just one or two tourism types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. tourism is ruin), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like.Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.

1) Data Introduction • The Rain in Australia Dataset is a tabular weather forecasting dataset, including daily weather information collected for approximately 10 years from various weather stations across Australia, and next-day precipitation (more than 1 mm, RainTomorrow).

2) Data Utilization (1) Rain in Australia Dataset has characteristics that: • Each row contains a variety of daily weather variables and target variables (RainTomorrow: Next Day RainTomorrow) such as date, region, highest/lowest temperature, precipitation, humidity, wind speed, and air pressure. • The data reflect multiple regions and various weather conditions, making them suitable for time series and spatial weather pattern analysis and the development of binary classification prediction models. (2) Rain in Australia Dataset can be used to: • Development of precipitation prediction models: Machine learning-based next-day precipitation prediction (whether an umbrella is required) models can be built using various weather variables and RainTomorrow labels. • Weather Patterns and Regional Analysis: By analyzing regional and seasonal weather variables and precipitation patterns, it can be used to establish customized weather strategies for each industry, such as climate change research and agriculture and tourism.

Geoscape G-NAF is the geocoded address database for Australian businesses and governments. It’s the trusted source of geocoded address data for Australia with over 50 million contributed addresses distilled into 15.4 million G-NAF addresses. It is built and maintained by Geoscape Australia using independently examined and validated government data.

From 22 August 2022, Geoscape Australia is making G-NAF available in an additional simplified table format. G-NAF Core makes accessing geocoded addresses easier by utilising less technical effort.

G-NAF Core will be updated on a quarterly basis along with G-NAF.

Further information about contributors to G-NAF is available here.

With more than 15 million Australian physical address record, G-NAF is one of the most ubiquitous and powerful spatial datasets. The records include geocodes, which are latitude and longitude map coordinates. G-NAF does not contain personal information or details relating to individuals.

Updated versions of G-NAF are published on a quarterly basis. Previous versions are available here

Users have the option to download datasets with feature coordinates referencing either GDA94 or GDA2020 datums.

Changes in the May 2025 release

• Nationally, the May 2025 update of G-NAF shows an overall increase of 47,194 addresses (0.30%). The total number of addresses in G-NAF now stands at 15,753,927 of which 14,909,770 or 94.64% are principal.

• At some locations, there are unit-numbered addresses that appear to be duplicate addresses. Geoscape is working to identify these locations and include these addresses as separate addresses in G-NAF. To facilitate this process, some secondary addresses have had the word RETAIL added to their building names. In the first instance, this process is being progressively rolled out to identified locations, but it is expected that the requirement for this will become ongoing.

• There is one new locality in G-NAF: Keswick Island, QLD.

• The source data used for generating G-NAF STREET_LOCALITY_POINT data in New South Wales has an updated datum and changed from GDA94 to GDA2020. This has resulted in updates to the STREET_LOCALITY_POINT geometry for approximately 91,000 records, however, more than 95% of these have moved less than a metre.

• Geoscape has moved product descriptions, guides and reports online to https://docs.geoscape.com.au.

Further information on G-NAF, including FAQs on the data, is available here or through Geoscape Australia’s network of partners. They provide a range of commercial products based on G-NAF, including software solutions, consultancy and support.

Additional information: On 1 October 2020, PSMA Australia Limited began trading as Geoscape Australia.

License Information

Use of the G-NAF downloaded from data.gov.au is subject to the End User Licence Agreement (EULA)

The EULA terms are based on the Creative Commons Attribution 4.0 International license (CC BY 4.0). However, an important restriction relating to the use of the open G-NAF for the sending of mail has been added.

The open G-NAF data must not be used for the generation of an address or the compilation of an address for the sending of mail unless the user has verified that each address to be used for the sending of mail is capable of receiving mail by reference to a secondary source of information. Further information on this use restriction is available here.

End users must only use the data in ways that are consistent with the Australian Privacy Principles issued under the Privacy Act 1988 (Cth).

Users must also note the following attribution requirements:

Preferred attribution for the Licensed Material:

_G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the _Open Geo-coded National Address File (G-NAF) End User Licence Agreement.

Preferred attribution for Adapted Material:

Incorporates or developed using G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the Open Geo-coded National Address File (G-NAF) End User Licence Agreement.

What to Expect When You Download G-NAF

G-NAF is a complex and large dataset (approximately 5GB unpacked), consisting of multiple tables that will need to be joined prior to use. The dataset is primarily designed for application developers and large-scale spatial integration. Users are advised to read the technical documentation, including product change notices and the individual product descriptions before downloading and using the product. A quick reference guide on unpacking the G-NAF is also available.

LGA based data for Country of Birth of Person by Year of Arrival in Australia, in Place of Enumeration Profile (PEP), 2016 Census. Count of persons born overseas (excludes overseas visitors). P10 is broken up into 3 sections (P10a - P10c), this section contains 'South Africa Year of arrival Before 1946' - 'Total Total'. Where arrival is stated as the year 2016 it corresponds to the period 1 January 2016 to 9 August 2016. The list of countries consists of the most common Country of Birth responses (excluding Australia) reported in the 2011 Census. The data is by LGA 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census.

This dataset provides values for INCOME SHARE HELD BY HIGHEST 10PERCENT WB DATA.HTML. reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The general purpose of my model is to identify three snake species in Australia. I will first use the Oxyuranus microlepidotus Taipan, which is referred to as the Inland Taipan (Billabong Sanctuary, n.d). I will then use the Oxyuranus scutellatus Taipan, which is referred to as the Coastal Taipan (Beatson, n.d). My third classification I will be using is the Pseudonaja textilis Brown-Snake, which is referred to as the Eastern-Brown-Snake (Wikipedia, 2024). I have created an object detection model. My data was derived from Google Images and iNaturalist. When searching for each snake, I typed in the name of the snake for each class. I mainly focused on getting clear-cut images that mainly focus on the head of the snake, since most of the differences between each focus there. I am hoping this project will reach out to audiences, such as any student interested in science, any professors that may be interested or be teaching Environmental Studies courses, and overall anyone interested in learning more about Australian snake species, since each of these species is some of the most dangerous in the world. My model will be valuable in being able to distinguish between the species, since each looks very similar. So, my overall goal of the project is to be able to identify the physical differences between the snakes, so scientists and educators can use this model to detect dangerous snakes in Australia. This model was created for a class assignment in AI and Natural History at St. Mary’s College of Maryland.

References: Beatson, A. C. (n.d.). Coastal Taipan. The Australian Museum. https://australian.museum/learn/animals/reptiles/coastal-taipan/#:~:text=Scientific%20name%3A%20Oxyuranus%20scutellatus,mungkan%20people%20of%20Cape%20York. Billabong Sanctuary. (n.d.). Discover the deadly beauty of the inland taipan. https://www.billabongsanctuary.com.au/inland-taipan/ Wikimedia Foundation. (2024b, March 13). Eastern Brown snake. Wikipedia. https://en.wikipedia.org/wiki/Eastern_brown_snake#:~:text=The%20eastern%20brown%20snake%20(Pseudonaja,snake%20in%20the%20family%20Elapidae.

Point-of-interest (POI) is defined as a physical entity (such as a business) in a geo location (point) which may be (of interest).

We strive to provide the most accurate, complete and up to date point of interest datasets for all countries of the world. The Australian POI Dataset is one of our worldwide POI datasets with over 98% coverage.

This is our process flow:

Our machine learning systems continuously crawl for new POI data
Our geoparsing and geocoding calculates their geo locations
Our categorization systems cleanup and standardize the datasets
Our data pipeline API publishes the datasets on our data store

POI Data is in a constant flux - especially so during times of drastic change such as the Covid-19 pandemic.

Every minute worldwide on an average day over 200 businesses will move, over 600 new businesses will open their doors and over 400 businesses will cease to exist.

In today's interconnected world, of the approximately 200 million POIs worldwide, over 94% have a public online presence. As a new POI comes into existence its information will appear very quickly in location based social networks (LBSNs), other social media, pictures, websites, blogs, press releases. Soon after that, our state-of-the-art POI Information retrieval system will pick it up.

We offer our customers perpetual data licenses for any dataset representing this ever changing information, downloaded at any given point in time. This makes our company's licensing model unique in the current Data as a Service - DaaS Industry. Our customers don't have to delete our data after the expiration of a certain "Term", regardless of whether the data was purchased as a one time snapshot, or via a recurring payment plan on our data update pipeline.

The main differentiators between us vs the competition are our flexible licensing terms and our data freshness.

The core attribute coverage for Australia is as follows:

Poi Field Data Coverage (%) poi_name 100 brand 13 poi_tel 49 formatted_address 100 main_category 94 latitude 100 longitude 100 neighborhood 3 source_url 55 email 10 opening_hours 41 building_footprint 60

The dataset may be viewed online at https://store.poidata.xyz/au and a data sample may be downloaded at https://store.poidata.xyz/datafiles/au_sample.csv

The Australian Census Longitudinal Dataset (ACLD) brings together a 5% sample from the 2006 Census with records from the 2011 Census to create a research tool for exploring how Australian society is changing over time. In taking a longitudinal view of Australians, the ACLD may uncover new insights into the dynamics and transitions that drive social and economic change over time, conveying how these vary for diverse population groups and geographies. It is envisaged that the 2016 and successive Censuses will be added in the future, as well as administrative data sets. The ACLD is released in ABS TableBuilder and as a microdata product in the ABS Data Laboratory.

The Census of Population and Housing is conducted every five years and aims to measure accurately the number of people and dwellings in Australia on Census Night.

Microdata products are the most detailed information available from a Census or survey and are generally the responses to individual questions on the questionnaire. They also include derived data from answers to two or more questions and are released with the approval of the Australian Statistician. The following microdata products are available for this longitudinal dataset: •ACLD in TableBuilder - an online tool for creating tables and graphs. •ACLD in ABS Data Laboratory (ABSDL) - for in-depth analysis using a range of statistical software packages.